modeling_mp_rna.py

import torch.nn as nn
from transformers import AutoModel

class CustomMPRNAForSequenceClassification(nn.Module):
    def __init__(self, base_model, num_labels):
        super().__init__()
        self.base_model = base_model
        self.num_labels = num_labels
        self.classifier = nn.Linear(base_model.config.hidden_size, num_labels)
        self.dropout = nn.Dropout(0.1)

    def forward(self, input_ids, attention_mask=None, labels=None):
        outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs[0][:, 0, :]
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        return {"logits": logits, "loss": loss} if loss is not None else {"logits": logits}